Intermediate Predictive Modeling Limited Attendance Seminar
December 7-8, 2017
1.5 Day Seminar
Registration for this event is now closed.
Registration for the Introductory Predictive Modeling LAS must be done separately.
Generalized Linear Models (GLMs) and such extensions as Generalized Additive Models and Multilevel/Hierarchical Models are today considered standard elements of the actuarial toolkit. As the size and complexity of available datasets continue to grow, actuaries and data scientists increasingly find it useful to adopt a broader array of statistical and machine learning tools. The Intermediate Predictive Modeling Limited Attendance Seminar [PMLAS-2] is intended for actuaries and data scientists who wish to complement a working knowledge of GLMs with other tools.
The techniques covered in this seminar fall into three classes. First, a variety of methods are available for achieving parameter shrinkage ("credibility weighting") within GLMs. This seminar will cover two highly practical and increasingly popular such methods: ridge and lasso regression. Second, machine learning techniques offer a powerful way of discovering and modeling interactions and nonlinear relationships in complex datasets. The seminar will cover some of the more popular machine learning techniques. Finally, the seminar will cover a variety of unsupervised learning methods (learning methods that do not involve a pre-specified outcome variable). Such methods are useful for identifying clusters and reduced-dimension data features in complex datasets. A common theme of all three classes of techniques is harnessing computing power to make sense of "wide" datasets, which commonly contain hundreds or even thousands of columns.
The seminar is designed to provide a general survey of techniques which complement the GLM framework. A substantive case study, as well as a number of smaller case studies, will complement the theoretical discussion with hands-on experience applying the techniques.
The seminar will help participants
- Gain an understanding of the strengths, weaknesses and typical uses of various statistical and machine learning techniques
- Gain a working knowledge of how to apply the predictive modeling techniques through the use of examples and case studies with real-world significance
- Gain an understanding of the inputs and outputs of the techniques and how to gauge the models’ effectiveness.
Note that this seminar presupposes working knowledge of Generalized Linear Models, as well as comfort manipulating data and fitting models in the R statistical computing environment.
- Shrinkage methods: Regularized regression (ridge and Lasso regression). Conceptual discussions of Bayesian regression and multilevel/hierarchical models will be provided to help contextualize the techniques.
- Tree-based Models: Classification and Regression Trees [CART], Random Forests, Gradient Boosted Trees
- Other supervised learning methods: Support Vector Machines, Multivariate Adaptive Regression Splines, ensemble methods
- Topics in unsupervised learning: k-means and hierarchical clustering, mixtures of Gaussians, principal components analysis. Other techniques such as matrix completion, association rules, and multidimensional scaling will be touched on time permitting.
Attendance is limited to a maximum of 35 students. Attendees will be selected on a first-registered, first-accepted basis. Participants are expected to bring their own laptop, loaded with the R software, to the seminar.
Jim Guszcza is the US Chief Data Scientist of Deloitte Consulting, and a member of Deloitte's Advanced Analytics and Modeling practice. He has built and helped design predictive models both in insurance and a variety of other public and private sector domains. Jim has also served as an assistant professor of Actuarial Science, Risk Management, and Insurance at the University of Wisconsin-Madison. A frequent contributor to actuarial seminars and publications, Jim has co-taught the Casualty Actuarial Society's Limited Attendance Seminar in Predictive Modeling each year since 2006. Jim is a Fellow of the Casualty Actuarial Society and currently serves on its Board of Directors. He is a graduate of St. John's College in Santa Fe, New Mexico and has a PhD in Philosophy from the University of Chicago.
David Shleifer is a manager with Deloitte Consulting, has 15 years of actuarial experience working with insurance companies and banks. For the past ten years he has focused on the design, development, validation, and testing of economic capital models, with emphasis on ORSA, Basel II and other regulatory issues. Prior to joining Deloitte, David was the senior member of a modeling team responsible for the development and deployment of underwriting risk models utilized for Solvency II compliance, capital allocation, strategic planning, and reinsurance strategy.
December 7, 2017 8:30 a.m. - 5:30 p.m.
December 8, 2017 8:30 a.m. - 12:00 p.m.
Note: Schedule is subject to change
Registration for this event is now closed.
|Registration Fees (in U.S. Dollars)||If received
on/before November 6, 2017
after November 6, 2017
|CAS Member, iCAS Member, or Active Candidate*||$975||$1,175|
* An Active Candidate is a non-CAS member who has attempted at least one actuarial exam in the last two years.
111 S Wacker Dr.
Chicago, IL 60606
A room block has not been designated for this meeting. The following hotels are near the Deloitte office:
Hyatt Place Chicago Downtown
28 North Franklin Street
Phone: (312) 955-0950
La Quinta Inn and Suites Downtown
1 South Franklin Street
Phone: (312) 558-1020
JW Marriott Chicago
151 West Adams Street
Phone: (312) 660-8200
Hyatt Centric the Loop Chicago
100 West Monroe Street
Phone: (312) 236-1234
W Chicago City Center
172 West Adams Street
Phone: (312) 332-1200
CONTINUING EDUCATION CREDIT
The CAS Continuing Education Policy applies to all ACAS and FCAS members who provide actuarial services. Actuarial services are defined in the CAS Code of Professional Conduct as "professional services provided to a Principal by an individual acting in the capacity of an actuary. Such services include the rendering of advice, recommendations, findings, or opinions based upon actuarial considerations." Members who are or could be subject to the continuing education requirements of a national actuarial organization can meet the requirements of the CAS Continuing Education Policy by satisfying the continuing education requirements established by a national actuarial organization recognized by the Policy.
Participants should claim credit commensurate with the extent of their participation in the activity. CAS members earn 1 CE Credit per 50 minutes of educational session time, not to include breaks or lunch.
Note: The amount of CE credit that can be earned for participating in this activity must be assessed by the individual attendee. It also may be different for individuals who are subject to the requirements of organizations other than the American Academy of Actuaries.
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